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Analýza anomálií v uživatelském chování / User Behavior Anomaly DetectionPetrovič, Lukáš January 2019 (has links)
The aim of this work is to create an application that allows modeling of user behavior and subsequent search for anomalies in this behavior. An application entry is a list of actions the user has executed on his workstation. From this information and from information about the events that occurred on this device the behavioral model for a specific time is created. Subsequently, this model is compared to models in different time periods or with other users' models. From this comparison, we can get additional information about user behavior and also detect anomalous behavior. The information about the anomalies is useful to build security software that prevents valuable data from being stolen (from the corporate enviroment).
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Investigating user behavior by analysis of gaze data : Evaluation of machine learning methods for user behavior analysis in web applications / Undersöka användarbeteende via analys av blickdataDahlin, Fredrik January 2016 (has links)
User behavior analysis in web applications is currently mainly performed by analysis of statistical measurements based on user interactions or by creation of personas to better understand users. Both of these methods give great insights in how the users utilize a web site, but do not give any additional information about what they are actually doing. This thesis attempts to use eye tracking data for analysis of user activities in web applications. Eye tracking data has been recorded, labeled and analyzed for 25 test participants. No data source except eye tracking data has been used and two different approaches are attempted where the first relies on a gaze map representation of the data and the second relies on sequences of features. The results indicate that it is possible to distinguish user activities in web applications, but only at a high error-rate. Improvement are possible by implementing a less subjective labeling process and by including features from other data sources. / I nuläget utförs analys av användarbeteende i webbapplikationer primärt med hjälp av statistiska mått över användares beteenden på hemsidor tillsammans med personas förökad förståelse av olika typer av användare. Dessa metoder ger stor insikt i hur användare använder hemsidor men ger ingen information om vilka typer av aktiviteter användare har utfört på hemsidan. Denna rapport försöker skapa metoder för analys av användaraktiviter på hemsidor endast baserat på blickdata fångade med eye trackers. Blick data från 25 personer har samlats in under tiden de utför olika uppgifter på olika hemsidor. Två olika tekniker har utvärderats där den ena analyserar blick kartor som fångat ögonens rörelser under 10 sekunder och den andra tekniken använder sig av sekvenser av händelser för att klassificera aktiviteter. Resultaten indikerar att det går att urskilja olika typer av vanligt förekommande användaraktiviteter genom analys av blick data. Resultatet visar också att det är stor osäkerhet i prediktionerna och ytterligare arbete är nödvändigt för att finna användbara modeller.
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Seeing Yourself Visualized as Data : A Qualitative Study on Users' Interactions and Perceptions of Data Visualizations in Digital Self-Tracking / Seeing Yourself Visualized as Data : A Qualitative Study on Users' Interactions and Perceptions of Data Visualizations in Digital Self-TrackingLepler, Liis January 2023 (has links)
Effective data visualization is essential for digital self-tracking to help users gain insights into their behavior and habits. Personalized visualizations engage users, making the self-tracking experience more meaningful. However, potential biases and limitations should be considered to ensure an accurate and objective self-tracking process. The study aims to examine users' interactions with visualized data in the digital self-tracking process and understand their perceptions of the accuracy and objectivity of personal data visualizations and the self-tracking processes on platforms that offer self-tracking features. These platforms include applications for tracking health and fitness, habits, music listening, book reading, and movie watching. The study employs a qualitative approach using semi-structured interviews with an ethnographic approach as a data collection method. This approach was selected to investigate users' interactions and opinions on data visualizations and the digital self-tracking process. The findings show that participants primarily use data visualizations and other personal visualizations as reminders, for comparisons, planning, and motivation. Although they do not extensively analyze the visualized data, participants report experiencing heightened self-awareness and motivation. Despite their awareness of potential inaccuracies and subjectivity in the visualizations and the self-tracking process, participants are willing to overlook these aspects due to the perceived benefits associated with the process. Moreover, participants generally express a level of trust in the accuracy of their visualized data.
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Fighting the Biggest Lie on the Internet : Improving Readership of Terms of Service and Privacy PoliciesZiegenbein, Marius-Lukas January 2022 (has links)
When joining a new service, in order to access its features, users are often required to accept the terms of service and privacy policy. However, the readership of these documents is mostly non-existent, leaving an information asymmetry, the imbalance of knowledge between two parties. Due to this, users are sacrificing their online data privacy without being aware of the consequences. The purpose of this work is to investigate the readership of terms of service and privacy policies among users of social media services. We implemented a prototype called ‘ShareIt’, which resembles a photo-sharing platform to gain insight about readership, behavior and effectiveness of our adjusted presentations of terms of service and privacy policies in regard to readership and comprehension. We conducted a survey experiment using the prototype with 31 participants and concluded, that 80,6% of our participants did not spend more than ten seconds in our terms of serviceand privacy policy. The observed behavior suggests, that social media users are used to sharing information on the internet which in addition to their trust towards online services leads to the aforementioned low readership. We presented adjustments to the presentation of terms of service and privacy policies which showed a slight tendency of higher engagement in comparison to the current way of accessing these documents. This result however, due to the lack of readership examined for our participants, has to remain debatable and needs further investigation.
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The Environmental Impact of Digital Marketplaces within the Circular Economy: A FrameworkBERDEN, SARAH R., GEBENDORFER, JOHANNES T. January 2020 (has links)
Digital marketplaces gained increased importance in the circular economy. Although their economic benefits are a popular subject in present literature, research is scarce on their environmental impact. Since economic motivations often collide with environmental motivations on digital marketplaces within the circular economy, rebound effects can occur. Therefore, it is crucial for companies to continuously evaluate the environmental value of their service. Consequently, this thesis aims at creating a framework to identify the environmental impact of digital marketplaces within the circular economy. Two important questions had to be answered in this research: How environmental impact assessment and user behavior can be combined to identify factors that influence the environmental impact of such digital marketplaces and how these factors can be incorporated into a framework. Based on a review of relevant literature on circular economy, environmental impact assessment and user behavior, a single case study was conducted on the digital marketplace of the urban logistics startup Tiptapp. Qualitative data was gathered by interviewing users. Respondents were divided into two groups depending on their role on the platform: ‘helpers’ (supply) and ‘advertisers’ (demand). The results indicated that mainly three factors influence the environmental impact: user behavior, user flow and direct interactions, and external factors. By designing the framework to outline the influencing factors and their relationship, an initial foundation has been built for digital marketplaces and their providers to evaluate the environmental impact of their services and to actively counteract concealed rebound effects. Future research is needed to facilitate generalizability of the proposed framework as well as data integration and accurate impact calculations. / Digitala marknadsplatser har ökat i betydelse för den cirkulär ekonomin. Även om deras ekonomiska fördelar är ett populärt ämne i nuvarande litteratur är forskning om deras miljöpåverkan knapp. Eftersom ekonomiska motiv ofta kolliderar med miljömässiga motiv på digitala marknadsplatser inom den cirkulära ekonomin kan rebound-effekter uppstå. Därför är det avgörande för företag att kontinuerligt utvärdera miljövärdet av deras tjänster. Följaktligen syftar den här studien till att skapa ett ramverk för att identifiera miljöpåverkan av digitala marknadsplatser inom cirkulär ekonomi. Två viktiga frågor måste besvaras i denna forskning: Hur miljökonsekvensbedömning och användarbeteende kan kombineras för att identifiera faktorer som påverkar miljöpåverkan av sådana digitala marknadsplatser och hur dessa faktorer kan integreras i ett ramverk. Baserat på relevant litteratur om cirkulär ekonomi, miljökonsekvensbedömning och användarbeteende har en fallstudie genomförts på den digitala marknadsplatsen Tiptapp, som är ett urbant logistik-startup. Kvalitativa data samlades in genom att intervjua användare. De svarande delades in i två grupper beroende på deras roll på plattformen: "helpers" (utbud) och "advertisers" (efterfrågan). Resultaten indikerade att främst tre faktorer har påverkan på miljön: användarbeteende, användarflöde och direkta interaktioner samt externa effekter. Genom att utforma ett ramverk för att beskriva de påverkande faktorerna och deras förhållande har en första grund byggts. Detta för att digitala marknadsplatser och deras leverantörer ska kunna utvärdera miljöpåverkan av deras tjänster och aktivt motverka dolda rebound-effekter. Framtida forskning behövs för att underlätta generaliserbarhet av det föreslagna ramverket samt dataintegration och exakta konsekvensberäkningar.
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Integrating Trust-Based Adaptive Security Framework with Risk Mitigation to enhance SaaS User Identity and Access Control based on User BehaviorAkpotor Scott, Johnson January 2022 (has links)
In recent years, the emerging trends in cloud computing technologies have given rise to different computing services through the Internet. Organizations across the globe have seized this opportunity as a critical business driver for computing resource access and utilities that will indeed support significant business operations. Embracing SaaS as a crucial business factor enhances corporate business strategy through economies of scale, easy manageability, cost-effectiveness, non-geographical dependence, high reliability, flexible resources, and fast innovation. However, this has also come with various risks due to the limitation of traditional user identity and access control solutions’ inability to effectively identify and manage cloud users’ authorization process when interacting with the cloud. The limit can result in a legitimate user account's impersonation to carry out malicious activities after the user account is compromised to go undetected since traditional solutions seldom function based on user behavior trust level behind any account. Furthermore, the limitation is a significant vulnerability to the cloud environment. This vulnerability is known to be exploited by threats that can eventually lead to substantial unacceptable risks that can undermine security principles or requirements such as confidentiality, integrity, and availability. Significant consequences of this risk are categorized into financial damages, legal implications, reputational damages, and regulatory implications to the cloud environment. As a result, a solution that could contribute to the remediation of these potential risks incurred due to the limitation of user identity and access control management was proposed and designed as User Behavior Trust-Based Adaptive Security framework. The design aims to enhance how cloud users' identity and access control might be managed effectively based on a user behavior trust context and adaptation of corresponding access control measures through adaptive security. The design capability was manifested by integrating it into the standard ISO/2705:2018 Risk Management process. Although, there have been several good information security frameworks such as ISO/IEC 27005:2018 and other technical countermeasures such as SaaS Identity & Access Management (IDaaS) to deal with this risk on the public cloud services. However, they are based on static mitigation approaches, so there is a solid need to shift towards a more dynamic strategical approach. The presented design work, User Behavior Trust-Based Adaptive Security framework, intends to serve as a proposed guideline for risk mitigation that would enhance user identity and access control limitations across the cloud. The solution functions by a trust modeling process that evaluates cloud user activities to compute a user behavior comprehensive trust degree. The resulting data is further used as input feeds parameters into a policy decision point process. The policy decision point process adapts the input parameters to user behavior trust level and behavior risk rating to determine the appropriate access control decision. Ultimately, the adaptive security solution consults the policy decision points to dynamically enforce the corresponding controls measures based on the access control decision received as input feed. The report also conducts a risk assessment process to identify vulnerabilities, threats, and risks related to user behavior trust level and risk rating regarding SaaS resources. Then adapt the mitigation solution, User Behavior Trust-Based Adaptive Security framework, as a possible risk treatment within the risk management process ISO/2705:2018. This report uses a design methodology derived from User Behavior Trust Modelling scientific research work, Gartner Adaptive Security Architecture Model, and eXtensible Access Control Markup Language's policy decision point concept. The design evaluates user behavior trust level by the trust modeling, while the integrated policy decision point processes the trust level to make the access control decision which is later enforced by the adaptive security solution. The report further adapts the risk management procedure ISO/2705:2018 to identify risk from user behavior and trust level, then implements the design solution as a possible risk treatment. The research findings were documented as Results and Discussion, where the functional and operational aspects of the designed framework were provided. In addition, the effects of applying the framework as a possible risk treatment solution were observed through conducting an ISO/2705:2018 risk management procedure. The notable outcome of a reduction of identified risk levels was an improvement in user attitude or behavior, which eventually increased user behavior trust level and reduced associated behavior risk. At the same time, the discussion detailed the interpretation of the results, implications, and limitation of the research, why the framework could be considered a remediation solution beyond the state-of-the-art for cloud user identity and access management—precisely by integrating user behavior, trust, policy decision making with adaptive security into risk management process to reduce IDM-associated risk in the SaaS. Finally, this study has outlined the significance of adopting the designed framework as a possible mitigation solution to enhance the shortcomings of user identity and access control management in the cloud. It has demonstrated that SaaS identified risk can be reduced to an acceptable level when user behavior and activities are taken seriously. Insight into the current trust state and associated risk level of cloud users are vital for continuous risk monitoring and reduction. The solution is to be used as a recommended guideline that might significantly contribute to the research community and information security field of cloud security. Future research direction to consider the possibility of simulating and transforming this conceptual and abstract framework into a real-world working solution due to research work limitations. The framework was designed based on recognized and accepted scientific and technological principles and concepts, from user behavior trust modeling, eXtensible access control markup language, and adaptive security architecture. In addition, to extend this concept to a future research area that will focus exclusively on application-processes behavior.
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Mining user behavior in location-based social networks / Mineração do comportamento de usuários em redes sociais baseadas em localizaçãoRebaza, Jorge Carlos Valverde 18 August 2017 (has links)
Online social networks (OSNs) are Web platforms providing different services to facilitate social interaction among their users. A particular kind of OSNs is the location-based social network (LBSN), which adds services based on location. One of the most important challenges in LBSNs is the link prediction problem. Link prediction problem aims to estimate the likelihood of the existence of future friendships among user pairs. Most of the existing studies in link prediction focus on the use of a single information source to perform predictions, i.e. only social information (e.g. social neighborhood) or only location information (e.g. common visited places). However, some researches have shown that the combination of different information sources can lead to more accurate predictions. In this sense, in this thesis we propose different link prediction methods based on the use of different information sources naturally existing in these networks. Thus, we propose seven new link prediction methods using the information related to user membership in social overlapping groups: common neighbors within and outside of common groups (WOCG), common neighbors of groups (CNG), common neighbors with total and partial overlapping of groups (TPOG), group naïve Bayes (GNB), group naïve Bayes of common neighbors (GNB-CN), group naïve Bayes of Adamic-Adar (GNB-AA) and group naïve Bayes of Resource Allocation (GNB-RA). Due to that social groups exist naturally in networks, our proposals can be used in any type of OSN.We also propose new eight link prediction methods combining location and social information: Check-in Observation (ChO), Check-in Allocation (ChA), Within and Outside of Common Places (WOCP), Common Neighbors of Places (CNP), Total and Partial Overlapping of Places (TPOP), Friend Allocation Within Common Places (FAW), Common Neighbors of Nearby Places (CNNP) and Nearby Distance Allocation (NDA). These eight methods are exclusively for work in LBSNs. Obtained results indicate that our proposals are as competitive as state-of-the-art methods, or better than they in certain scenarios. Moreover, since our proposals tend to be computationally more efficient, they are more suitable for real-world applications. / Redes sociais online (OSNs) são plataformas Web que oferecem serviços para promoção da interação social entre usuários. OSNs que adicionam serviços relacionados à geolocalização são chamadas redes sociais baseadas em localização (LBSNs). Um dos maiores desafios na análise de LBSNs é a predição de links. A predição de links refere-se ao problema de estimar a probabilidade de conexão futura entre pares de usuários que não se conhecem. Grande parte das pesquisas que focam nesse problema exploram o uso, de maneira isolada, de informações sociais (e.g. amigos em comum) ou de localização (e.g. locais comuns visitados). Porém, algumas pesquisas mostraram que a combinação de diferentes fontes de informação pode influenciar o incremento da acurácia da predição. Motivado por essa lacuna, neste trabalho foram desenvolvidos diferentes métodos para predição de links combinando diferentes fontes de informação. Assim, propomos sete métodos que usam a informação relacionada à participação simultânea de usuários en múltiples grupos sociais: common neighbors within and outside of common groups (WOCG), common neighbors of groups (CNG), common neighbors with total and partial overlapping of groups (TPOG), group naïve Bayes (GNB), group naïve Bayes of common neighbors (GNB-CN), group naïve Bayes of Adamic-Adar (GNB-AA), e group naïve Bayes of Resource Allocation (GNB-RA). Devido ao fato que a presença de grupos sociais não está restrita a alguns tipo de redes, essas propostas podem ser usadas nas diversas OSNs existentes, incluindo LBSNs. Também, propomos oito métodos que combinam o uso de informações sociais e de localização: Check-in Observation (ChO), Check-in Allocation (ChA), Within and Outside of Common Places (WOCP), Common Neighbors of Places (CNP), Total and Partial Overlapping of Places (TPOP), Friend Allocation Within Common Places (FAW), Common Neighbors of Nearby Places (CNNP), e Nearby Distance Allocation (NDA). Tais propostas são para uso exclusivo em LBSNs. Os resultados obtidos indicam que nossas propostas são tão competitivas quanto métodos do estado da arte, podendo até superá-los em determinados cenários. Ainda mais, devido a que na maioria dos casos nossas propostas são computacionalmente mais eficientes, seu uso resulta mais adequado em aplicações do mundo real.
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電子書閱讀器之使用者行為資料分析 / Analysis and Interpretation of E-reader User Log闕建堡, Chueh, Chien Pao Unknown Date (has links)
由於資訊科技與顯示技術的大幅進步,電子書閱讀裝置成為近來十分受矚目的產品,然綜觀目前國內外電子書的發展,多以較具消費能力的上班族為對象,較少以基礎教育的學生為目標使用者。
本研究針對使用於教育市場的電子書閱讀器進行前導性的實驗,並透過自行設計的使用者介面、數位學習平台及結合傳統問卷與自動化使用者行為蒐集程式,結合質化研究與量化分析的優點,以瞭解最真實的學生使用習慣與方式。
本研究目的在於探索電子書閱讀器進入高中學生的實際課堂學習與日常生活之中,對學生的學習經驗或閱讀習慣所產生之影響,並進一步瞭解從學生最初接觸電子書的新鮮試用期,到後續逐漸習慣使用或棄用電子書等不同階段的行為變化。透過此研究,瞭解電子書閱讀器應用於教育市場的接受程度及可能潛力,做為未來電子書設計、開發與推廣使用的可行性參考依據。 / Recently, e-book reading devices based on electronic ink (e-ink) have gained a lot of attention thanks to the rapid advances in both information and display technology. However, current products are mainly targeted on general users for their daily reading activities. Research on introducing the e-reading device into high school or college campuses has commenced only quite recently.
In this thesis, we exploit the potential of employing e-book reading devices in facilitating learning in a high school campus. We have custom-designed the user interface as well as the textbook content to suit the needs of this particular user group. The unique opportunity of having access to the hardware device, software design and potential users creates an ideal experimental platform for us to unbiasedly investigate the role of this new technology through a long-term user behavior collection and analysis process.
This study aims to explore how the introduction of e-book reader into high school campus influences the students’ learning and daily life. We documented changes in the users’ e-book reading behavior during the course of a six-month experiment, corresponding to progressive stages of growing familiarity and comfort with using the machine. We found that e-book reader does help the students to develop a habit of mobile reading. Its effect exceeds our expectation of achieving digital learning. We hope that the findings presented in this thesis can be useful to teachers and system designers to develop new types of teaching materials and activities by taking advantages of the characteristics of this new technology.
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Safety evaluation of converging chevron pavement markingsPratyaksa, Prabha 09 April 2013 (has links)
Chevron pavement markings have seen rising interest in the United States as a means to reduce speeds at high-speed locations and improve safety performance. In Atlanta, there are two freeway-to-freeway ramps where chevron markings are being used. A previous study analyzed before-and-after speed data at these ramps and found only a modest reduction on overall vehicle speeds. However, a cursory crash analysis indicated that the ramps had crash reductions of over 60%, suggesting that safety benefits exist even though vehicle speeds are not significantly affected. This research aims to evaluate the safety performance of chevron markings on the two ramps in Atlanta, GA in order to quantify the potential impact of the treatment on safety and to understand the mechanism by which the treatment influences safety.
This thesis begins with a literature review covering topics in human factors in safety, past uses of different types of pavement markings, and methods in using crash databases and police reports in accident studies. Next, the thesis presents an in-depth before and after analysis of crash data from crash databases and police reports provided by the Georgia Department of Transportation. And finally, the thesis concludes with a summary of findings and a discussion of further research needs.
The results verified that there were 73% and 61% crash reductions in the two study ramps. Chevron markings appear to have benefitted all types of crashes and that they are possibly serving as a warning to drivers of potential upcoming hazards. Unavailability of a number of police reports and errors in crash databases were limitations to this study, and ultimately, new sites should be selected carefully and further studies need to be performed to better understand the treatment's benefits.
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手機使用者於電量管理之行為模式分析 / User Behavior Analysis of Power Management from Smart-Phone User Logs張錦生, Chang, Chin Sheng Unknown Date (has links)
資訊科技的進步與智慧型手機的普及,使得人們通訊方式改變,生活也更加依賴智慧型手機。然而,電池技術卻未能支援智慧型手機長時間使用,因此手機使用者在電量管理上的行為就變得相對重要。欲研究探討手機使用者的電量管理行為模式,須建立一個包含軟、硬體及使用者的實驗平台,本研究採用經麻省理工學院驗證的Funf Framework開放性原始碼框架,作為蒐集使用者操作紀錄資料,以情境假設觀察這些資料,定義出各情境行為模式的特徵,並根據實驗數據進行所有資料驗證。根據實驗結果,大致歸納出電量管理行為模式,此結果可提供使用者使用手機在電量管理上參考,或發展智慧型電量管理應用程式,以最佳化電量管理。 / The innovation of information technology and the spread of smart phones are changing the way that people communicate and how their livings rely on smart-phones. However, the technology of battery nowadays is still insufficient to meet the need of heavy smart-phones users; therefore, it be-comes relatively important to observe and analyze the user behavior on power management. This research aims to study the patterns of user be-havior on power management by building an experimental platform with appropriate software, hardware and users. We use the Funf Open Sensing Framework, which is originally developed at the MIT Media Lab, to collect user logs on smart phones. We have observed collected data under contex-tual assumptions, identified characteristics within the context of each be-havior pattern, and validated with the experimental data. With the result of the experiment, several patterns of power management have been classified. The experimental result can be used as a reference for the users to manage battery life, or for developing applications on smart power management that best optimizes energy consumption.
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